code: def __init__(
      self,
      num_features,
      eps=1e-5,
      momentum=0.1,
      affine=True,
      rmax=3.0,
      dmax=5.0,
  ):
      super(BatchReNorm1d, self).__init__()
      self.num_features = num_features
      self.eps = eps
      self.momentum = momentum
      self.affine = affine
      self.rmax = rmax
      self.dmax = dmax
  
      if self.affine:
          self.weight = Parameter(torch.Tensor(num_features))
          self.bias = Parameter(torch.Tensor(num_features))
      else:
          self.register_parameter("weight", None)
          self.register_parameter("bias", None)
  
      self.register_buffer("running_mean", torch.zeros(num_features))
      self.register_buffer("running_var", torch.ones(num_features))
      self.register_buffer("r", torch.ones(num_features))
      self.register_buffer("d", torch.zeros(num_features))
  
      self.reset_parameters()
  
file_path: pywick/pywick/functions/batchrenorm.py
imports: import torch
  from torch.nn.modules import Module
  from torch.nn.parameter import Parameter
label_desc: Use image node_id 1 to create a new BatchReNorm1d object from inherited base classes: Module with example: obj = BatchReNorm1d(num_features, eps, momentum, affine, rmax, dmax)